Name
Abstract | ||
|---|---|---|
The problem of clustering a set of data is a textbook machine learning problem, but at the same time, at heart, a typical optimization problem. Given an objective function, such as minimizing the intracluster distances or maximizing the inter-cluster distances, the task is to find an assignment of data points to clusters that achieves this objective. In this paper, we present a constraint programming model for a centroid based clustering and one for a density based clustering. In particular, as a key contribution, we show how the expressivity introduced by the formulation of the problem by constraint programming makes the standard problem easy to be extended with other constraints that permit to generate interesting variants of the problem. We show this important aspect in two different ways: first, we show how the formulation of the density-based clustering by constraint programming makes it very similar to the label propagation problem and then, we propose a variant of the standard label propagation approach. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-662-49224-6_9 | Lecture Notes in Computer Science |
Field | DocType | Volume |
Constraint satisfaction,Mathematical optimization,Correlation clustering,Computer science,Constraint programming,Algorithm,Constraint satisfaction dual problem,Cutting stock problem,Constrained clustering,Cluster analysis,Constraint logic programming | Conference | 9509 |
ISSN | Citations | PageRank |
0302-9743 | 2 | 0.41 |
References | Authors | |
17 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valerio Grossi | 1 | 30 | 5.18 |
| Anna Monreale | 2 | 581 | 42.49 |
| Mirco Nanni | 3 | 1412 | 84.47 |
| Dino Pedreschi | 4 | 3083 | 244.47 |
| Franco Turini | 5 | 842 | 101.81 |